Research and Implementation of Automate Segmentation for Low Contrast Medical Images

2009 ◽  
Author(s):  
Qing Chang ◽  
Jichao Yan
Keyword(s):  
Medical Images ◽  
Low Contrast ◽  
Automate Segmentation

Enhancement Algorithm Combining Pseudocolor Processing and Marker-Based Watershed Transform for Medical Image

2013 ◽  
Vol 655-657 ◽  
pp. 1953-1956
Author(s):  
Chun Yu Ning ◽  
Shu Fen Liu
Keyword(s):  
Medical Image ◽  
Clinical Medicine ◽  
Medical Images ◽  
Fusion System ◽  
Watershed Transform ◽  
Low Contrast ◽  
Different Types ◽  
Two Parameters ◽  
Medical Image Enhancement ◽  
Enhancement Algorithm

Medical images play a very important role in clinical medicine. They often have the features of low contrast, narrow gray scale and edge blurring between tissues. Aimed at the disadvantages, the paper presents a new medical image enhancement algorithm combining pseudocolor processing and marker-based watershed transform. The new algorithm and traditional pseudocolor processing in frequency domain are implemented using MATLAB, and their effectivity is evaluated by two parameters, mean and entropy. Results show that the proposed enhancement algorithm can improve the visual effect of different types of medical images, especially for the tissues in the low contrast regions in the image. The output image is more suitable for doctor’s diagnosing. The algorithm has been used in the medical image fusion system we developed, and the performance is very satisfactory.

scholarly journals Extraction and Visualization of Ocular Blood Vessels in 3D Medical Images Based on Geometric Transformation Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zhike Zhang ◽  
Shuixin Zhang ◽  
Hongyu Feng
Keyword(s):  
Blood Vessels ◽  
Input Signal ◽  
Medical Images ◽  
Vessel Segmentation ◽  
New Method ◽  
Geometric Transformation ◽  
High Contrast ◽  
Low Contrast ◽  
Resonance Response ◽  
Local Image

Data extraction and visualization of 3D medical images of ocular blood vessels are performed by geometric transformation algorithm, which first performs random resonance response in a global sense to achieve detection of high-contrast coarse blood vessels and then redefines the input signal as a local image shielding the global detection result to achieve enhanced detection of low-contrast microfine vessels and complete multilevel random resonance segmentation detection. Finally, a random resonance detection method for fundus vessels based on scale decomposition is proposed, in which the images are scale decomposed, the high-frequency signals containing detailed information are randomly resonantly enhanced to achieve microfine vessel segmentation detection, and the final vessel segmentation detection results are obtained after fusing the low-frequency image signals. The optimal stochastic resonance response of the nonlinear model of neurons in the global sense is obtained to detect the high-grade intensity signal; then, the input signal is defined as a local image with high-contrast blood vessels removed, and the parameters are optimized before the detection of the low-grade intensity signal. Finally, the multilevel random resonance response is fused to obtain the segmentation results of the fundus retinal vessels. The sensitivity of the multilevel segmentation method proposed in this paper is significantly improved compared with the global random resonance results, indicating that the method proposed in this paper has obvious advantages in the segmentation of vessels with low-intensity levels. The image library was tested, and the experimental results showed that the new method has a better segmentation effect on low-contrast microscopic blood vessels. The new method not only makes full use of the noise for weak signal detection and segmentation but also provides a new idea of how to achieve multilevel segmentation and recognition of medical images.

scholarly journals Nonlocal Means-Based Denoising for Medical Images

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Ke Lu ◽  
Ning He ◽  
Liang Li
Keyword(s):  
Medical Imaging ◽  
Medical Images ◽  
Random Noise ◽  
Imaging Analysis ◽  
Low Contrast ◽  
Nonlocal Means ◽  
Noisy Pixel ◽  
Improved Performance ◽  
Adaptive Denoising ◽  
State Of Art

Medical images often consist of low-contrast objects corrupted by random noise arising in the image acquisition process. Thus, image denoising is one of the fundamental tasks required by medical imaging analysis. Nonlocal means (NL-means) method provides a powerful framework for denoising. In this work, we investigate an adaptive denoising scheme based on the patch NL-means algorithm for medical imaging denoising. In contrast with the traditional NL-means algorithm, the proposed adaptive NL-means denoising scheme has three unique features. First, we use a restricted local neighbourhood where the true intensity for each noisy pixel is estimated from a set of selected neighbouring pixels to perform the denoising process. Second, the weights used are calculated thanks to the similarity between the patch to denoise and the other patches candidates. Finally, we apply the steering kernel to preserve the details of the images. The proposed method has been compared with similar state-of-art methods over synthetic and real clinical medical images showing an improved performance in all cases analyzed.

scholarly journals Segmentation of Low Contrast Satellite and Medical Images Based on Level Set Function with Harmony Search Optimization Algorithm

2019 ◽  
Vol 9 (1) ◽  
pp. 7306-7313
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Medical Images ◽  
Search Algorithm ◽  
Harmony Search ◽  
Threshold Level ◽  
Harmony Search Algorithm ◽  
Low Contrast ◽  
Level Set Function ◽  
Set Function

The main aim of digital image segmentation for portioned the image in to its constituents parts for getting information regarding features of image also used to get pathological details from medical images. The literature available from last two decades the important scheme for image segmentation is with Level Set technique, multilevel thresholding of gray scale on histogram of image is also a traditional method of image segmentation. In this paper low contrast images from medical and satellite images considered for image segmentation to extract features. This paper puts forward a novel image segmentation method via Level Set Function along with BiHistogram Equalization based on Harmony Search Algorithm(LSFBHEHS). The Selective Binary and Gaussian Filtering Regularised Level Set (SBGFRLS) is efficient novel region based Active Contour Model, it uses a novel region-based signed pressure force (SPF) function, it can adeptly halt the contours at blurred edges and weak edges. Other important advantage is internal and external boundaries can be distinguished by fixing the initial contour may be anyplace in the considered image. This method is resourceful but requires more time and inefficient for segmentation of low contrast images. This problem is rectified by applying bi-histogram equalization(BHE) image enhancement method prior to Level Set, it can be treated as pre-processing. In BHE technique of image enhancement, the image histogram is partitioned into two divisions based optimized gray level threshold , and equalize each part of histogram separately and combined later. To find the optimized threshold level to slice the histogram into two parts, Otsu’s multilevel thresholding method used to find threshold level, to find optimized thresholding level Harmony Search Algorithm(HSA) is implemented to maximize inert class variance as objective function. For evaluating the proposed method and SBGFRLS, the qualitative measured used like Dice similarity index, Measure of Enhancement(EME) and time required, for experimentation numerous low contrast satellite and medical images are considered, results clarified that the proposed method is more efficient for low contrast and inhomogeneous images.

A Novel Medical Image Enhancement Algorithm Based on Dyadic Wavelet Transform

2014 ◽  
Vol 513-517 ◽  
pp. 2726-2729 ◽  
Author(s):  
Jia Li ◽  
Yun Feng Yang ◽  
Peng Xiao Wang ◽  
Bo Li
Keyword(s):  
Wavelet Transform ◽  
Image Enhancement ◽  
High Frequency ◽  
Medical Image ◽  
Medical Images ◽  
Low Contrast ◽  
Frequency Information ◽  
Dyadic Wavelet ◽  
Dyadic Wavelet Transform ◽  
Medical Image Enhancement

In clinical application, medical images tend to be low contrast, bigger or more speckle noise, and they will affect the effective use of medical images. Medical image enhancement can solve the problem of the low contrast of the image, so as to get more clear details of images. An algorithm of the medical image enhancement is proposed based on the binary wavelet transform in the paper. Firstly, the medical image was carried through dyadic wavelet transform, then the high-frequency information was de-noised, and then to enhance the high frequency information which was de-noised; at last, the enhanced high-frequency sub-images and the low-frequency sub-images were reconstructed by inverse dyadic wavelet transform. Finally, a better visual effect can be got by a subsection grayscale transform. The experiment results show the enhanced effect of proposed method is better than those of the wavelet transform.

scholarly journals MMTDNN: Multi-view Massive Training Deep Neural Network for Segmentation and Detection of Abnormal Tissues in Medical Images

2020 ◽  
Author(s):  
Hassan Khastavaneh ◽  
Hossein Ebrahimpour Komleh
Keyword(s):  
Skin Lesion ◽  
Medical Images ◽  
Complex Structure ◽  
Experimental Result ◽  
Dice Similarity Coefficient ◽  
Automated Segmentation ◽  
Lesion Segmentation ◽  
Tissue Segmentation ◽  
Low Contrast ◽  
Training Strategy

Purpose: Automated segmentation of abnormal tissues in medical images is considered as an essential part of those computer-aided detection and diagnosis systems which analyze medical images. However, automated segmentation of abnormalities is a challenging task due to the limitations of imaging technologies and complex structure of abnormalities, including low contrast between normal and abnormal tissues, shape diversity, appearance inhomogeneity, and the vague boundaries of abnormalities. Therefore, more intelligent segmentation techniques are required to tackle these challenges. Materials and Methods: In this study, a method, which is called MMTDNN, is proposed to segment and detect medical image abnormalities. MMTDNN, as a multi-view learning machine, utilizes convolutional neural networks in a massive training strategy. Moreover, the proposed method has four phases of preprocessing, view generation, pixel-level segmentation, and post-processing. The International Symposium on Biomedical Imaging (ISBI)-2016 dataset is used for the evaluation of the proposed method. Results: The performance of the proposed method has been evaluated on the task of skin lesion segmentation as one of the challenging applications of abnormal tissue segmentation. Both qualitative and quantitative results demonstrate outstanding performance. Meanwhile, the accuracy of 0.973, the Jaccard index of 0.876, and the Dice similarity coefficient of 0.931 have been achieved. Conclusion: In conclusion, the experimental result demonstrates that the proposed method outperforms stateof-the-art methods of skin lesion segmentation.

scholarly journals A Framework for Multi-Threshold Image Segmentation of Low Contrast Medical Images

2021 ◽  
Vol 38 (2) ◽  
pp. 309-314
Author(s):  
Ahmed Elaraby ◽  
Ismail Elansary
Keyword(s):  
Membership Function ◽  
Medical Images ◽  
Vital Role ◽  
Performance Evaluations ◽  
Foreground Object ◽  
Selection Mechanism ◽  
Threshold Selection ◽  
Low Contrast ◽  
Segmentation Accuracy ◽  
And Performance

Accurate medical images segmentation plays a vital role in contouring during diagnosis and treatment planning. To improve the segmentation accuracy in low contrast images, we propose a method by combining Hill entropy and fuzzy c-partition. Here, using membership function, an image is first transformed into fuzzy domain. Subsequently, the fuzzy Hill entropies are defined for foreground (object) and background. Next, the total fuzzy Hill entropy is maximized to compute the accurate threshold; this process is employed to calculate a proper parameter combination of membership function. This Hill entropy is then optimized to acquire an image threshold by Differential Evolution “DE” optimization algorithm. The key benefit of the presented approach is that it considers the information of background and object as well as interactions between them in threshold selection mechanism. The results and performance evaluations show the better accuracy of our technique over other existing approaches.

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